Boya Lin
About Boya Lin
Boya Lin is a Data Science Graduate Consultant at ABC Supply Co. Inc. in Chicago, Illinois, where he has worked since 2021, focusing on customer credit default prediction models and feature engineering techniques.
Work at ABC Supply Co. Inc.
Boya Lin has been employed at ABC Supply Co. Inc. as a Data Science Graduate Consultant since 2021. In this role, Lin focuses on data analysis and model development in Chicago, Illinois. Responsibilities include implementing WOE Transformation and Toad’s decision tree binning for feature engineering using Python. Lin has developed customer credit default prediction models utilizing logistic regression, random forest, and CatBoost, contributing to the company's data-driven decision-making processes.
Previous Experience in Data Science
Prior to joining ABC Supply Co. Inc., Boya Lin worked at NASA Jet Propulsion Laboratory as a Data Science Graduate Consultant for two months in 2021. Additionally, Lin served as a Risk Analyst Intern at Caitong Securities Ltd for two months in 2020 and as an Analyst Intern at Alibaba Group for one month in the same year. These roles provided valuable experience in data analysis and risk assessment.
Education and Expertise
Boya Lin holds a Master of Science in Analytics from Northwestern University, where studies were completed from 2021 to 2022. Prior to this, Lin earned a Bachelor of Science in Applied Mathematics and Statistics from the University of California, Davis, from 2017 to 2021. This educational background equips Lin with strong analytical skills and a solid foundation in data science methodologies.
Technical Skills and Methodologies
Boya Lin has demonstrated proficiency in various data science methodologies, particularly in feature engineering and model optimization. Lin has implemented WOE Transformation and Toad’s decision tree binning for feature engineering. Additionally, Lin has performed feature selection based on Gini index, information value, and correlation to enhance model performance. These skills are essential for developing predictive models and conducting data analysis.